Enterprise Decision Management - a Weblog

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September 28, 2006

Turning Transactions into Decisions with EDM

Rob Karel and Keith Gile of Forrester recently published a report on "Turning Transactions Into Decisions". This was mostly about data integration options in operational BI situations but as I read it I found some interesting tidbits. They make the point that the information being integrated/delivered must facilitate decision-making and that there are typically many decision points in a process. I could not agree more - indeed this focus on decision services within a process is key to enterprise decision management. As they continue on they draw a distinction between manual decisions (that require BI support) and automated ones. However their example of manual decision - deciding about credit-line increases - is actually a prime example of a decision that is increasingly automated in an EDM-like way! Indeed I have blogged about using rules and analytics to automate credit decisions at a UK bank and about how similar loan decisions can also be automated. Their other example, routing of calls, is a typical automated decision and one that can also be improved using EDM.

Now when you are talking about automated decisions, you need executable rules (procedures, policies, regulations) and analytic insight (embedded into decision execution) to replace the policy manuals and reports that would support a manual decision. This is why EDM uses both rules and analytics to focus on automating and improving operational decisions. If the frequency of your decision is high and the time for taking the decision is short then automate it using EDM.

Another point to note here is that using analytic techniques to build the rules you need can mean not needing so much data at execution time - you process the high data volumes/multiple data sources to build the models but the models may not require so much data. For example, there was a great story in Blink about the small number of data elements that actually made a difference to a specific medical decision. Using an EDM approach might actually reduce the need for data integration in your operational systems. The article also makes some good points about data latency (see my comment on a Richard Hackathorn article) and made me think of this post on leveraging customer intelligence through decisioning.

Technorati Tags: analytics, BI, BRE, BRMS, business rule, business rules, decision automation, EDM, Enterprise Decision Management, Forrester, predictive analytics, operational BI

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Posted by James Taylor at 9:41 AM | Comments (0)

September 26, 2006

Great article on business rules and business process

Bruce Silver just wrote a great article on Intelligent Enterprise "Analysis: Where Rules Management and BPM Meet". Highly recommended for anyone interested in the ongoing debate around business rules and business process.

Technorati Tags: BPM, BRE, BRMS, business process management, business rule, business rules, BPMS

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Posted by James Taylor at 1:02 PM | Comments (0)

Customer-centric IT requires enterprise decision management

I was reading "How to Prepare IT for the Customer-Aligned Enterprise" by Michael Maoz of Gartner and thought he made some great points.

The impact on IT planners is that they will need to work more closely with business owners to invest in more-dynamic applications with declarative capabilities, integrated with (and/or gradually replacing) the task-based (procedural) applications in use today.

Absolutely. The need for dynamic, or agile, applications is going to grow as companies try and become more customer-centric.Building agility into applications means, at least in part, making sure that the decisions within the application can be rapidly evolved to reflect changing realities. The use of business rules to provide "declarative capabilities" is a key element of this.

Michael goes on to talk about the current generation of college-age people - like my slug for instance - who are intimately familiar with technology, used to getting all the information they want on the net and open to loose networks of Friends-Of-Friends. As Michael says "They are puzzled when an organization fails to
recognize them when they return to a store, call center, ATM or Internet site" and so not only will companies have to provide much more information, they will also have to do a much better job of allowing customers to choose channels and for personalizing those channels.

Michael also makes some good points about the current generation of enterprise applications:

"Most business applications in the hands of employees who interact with customers are little more than data entry, presentation and retrieval systems."
"These applications fail, however, to dynamically reconfigure based on dynamic declarative rules."

These "dumb" enterprise applications must be given more "intelligence" if they are to help staff interact intelligently with customers who are both sophisticated users of technology and intolerant of process/organizational boundaries.

Enterprise Decision Management can deliver some of what's needed - even I would not pretend it can generate all of it - by improving the precision of decisions using analytics to better target and assist customers, by improving the agility of systems to make them easier to change so they can keep up with this kind of customer, and by delivering consistency across channels and time so that customers can choose how to get the service they want. The use of business rules and analytics can also deliver deep personalization and handle complex consequences of events as they happen. I presented on using EDM to improve the customer experience at the Teradata Partners show recently.

Technorati Tags: analytics, business rule, business rules, CRM, customer experience, decision automation, EDM, Enterprise Decision Management, Gartner, personalization, predictive analytics

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Posted by James Taylor at 12:52 PM | Comments (0)

September 25, 2006

Is there really ANY ROI from BI?

I have been talking with folks a lot about the issue of return on investment (ROI) when it comes to Business Intelligence (ROI) and, quite timely, I saw this report from Knightsbridge about the ROI of Business Intelligence when I was scanning BusinessWeek magazine. Now I don't want to imply anything about the quality of work Knightsbridge does for its clients as I have absolutely no data but the study left me saying "yes, but..." as it defined success with BI as "getting more of a return that I expected". No-one was asked for hard ROI numbers, just if they had met or exceeded their expectations. Now this is a little self referential - I say it did better than I expected it to - and fails to account for a known issue with BI projects - that no-one defines exactly how an ROI is going to be generated! The report had some good advice on making BI successful but failed, in my mind, to show any kind of real BI.

While I was pondering this I saw a report in ComputerWorld on BI Home Runs. As far as I can tell these "Home Runs" were selecting by the magazine, and presumably by the BI vendors who participated, as exemplars of the BI approach. All I could think of as I read these was "That's it? That's the best you could do?"

These cases were not impressive to me as examples of the best of BI. That's not to say that the companies profiled had not done a good job at what they were attempting - they mostly had - but that the basic BI approach was either flawed or at least very timid. Let's consider each of them in turn.

• 1-800 Contacts: Fine-tuning a Sales Strategy
  A classic example of managing metrics and displaying them. But where are the analytics to derive insight from this information? How could best practices and policies be overlain on top of these analyses? Why would you not focus on the operational decision - next best action for the call center - and give the call center agents the ANSWER not just data about the question? An EDM approach would have focused resources on giving the call center agents better offers when an offer was appropriate, better non-offer actions when that was called for, and would not have required the agent to worry about that thus enabling a focus on the conversation and improving the customer experience.
• Alliant Energy: Viewing Fine Details in Financial Data
  A good example of a real BI one as this is strategic management - applying insight to a low volume decision
• Allstate Insurance: Getting Fast Help to Those in Need
  This could be an example of BI but what about using in-line analytics and rules to process the claims at once? Why manage them in a data warehouse first? Why not just focus on the operational decision, pay the claim as an emergency or not, and use location intelligence, rules about what classifies as an emergency and perhaps analytics to do this as the claim arrives?
• BNSF Railway: Tracking Finances on the Rails
  This sounds like a nice dashboard and is a good performance management example.
  But what about improving operations - are there ways to use this information and insight automatically to route trains, schedule maintenance, manage staffing so as to reduce costs? If you applied EDM to this problem you would see a much greater return as it would improve the large number of small decisions a railway takes.
• Dreyfus: Testing a Campaign's Success
  While I am sure this generated a return, all I could think was "What a basic kind of approach". Finding out that a campaign was a big success among customers who already owned another specific fund is pretty basic. Analytics would give you much greater precision on this (I suspect that there is much more potential granularity in segmenting their customers than "own fund/don't own fund" that would help target the offers. In addition, if one thought of this as an Enterprise Decision Management problem, one would think about how to automate the decision of both which customer to mail and what to mail them so as to personalize and target the offer. Sending the same offer to a simple subset is a start, but only a start. Applying an EDM approach to a similar problem (getting existing customers to buy a product from a category they never bought from before) a retailer got 17% response rates or a 2000% lift from their existing process. There's a bunch of posts on this topic in the Marketing section.
• Eastern Mountain Sports: Getting Smarter with Each Sale
  Nice example of using BI for strategy assessment and then replicating it. I think they miss something by not thinking about this as an operational cross-sell decision, though. What about the website or the call center? If they took an EDM approach to build the best cross-sell option into a decision engine then they would be able to get the same increase across all channels much more quickly and automatically. Indeed there might not even be a need to train new staff as the till could make the recommendation and explain it.
• Emergency Medical Associates: Measuring the Emergency Room's Pulse
  Good example of performance management and strategic redesign based on BI. A comment in the story struck me as particularly relevant "Teaching people how to act upon data and analytics is harder than building the tools that report the data". Perhaps this means you should not attempt to teach people how to use the data and analytics but instead focus on automating key operational decisions and using medical rules/analysis of data to alert/warn staff with respect to specific transactions. This is what an EDM focus would do - develop decision automation that helps the staff as they go without requiring them to become analytically sophisticated.
• Highmark Inc.: Detecting a Web of Fraud
  Detecting fraud is a classic use for EDM - applying rules and analytic insight to transactions to tell which ones are fraudulent. Now BI can help in the research of fraud and perhaps in pattern identification but to catch fraud early enough to do something about it you need an EDM approach. You need to think about applying inline analytic models and expert rules as a transaction comes in, you need to make an operational decision that this transaction is suspicious enough to be treated differently. Indeed this kind of system exists - check out this post on healthcare fraud detection - and the rates of return are much higher than in "pay and chase".
• Hillman Group: Making Price Adjustments on the Fly
  While the story seems like a fairly classic BI story, I wonder if more could be done. As Dan Everett of Ventana Research says market profiling and segmentation is crucial. An EDM approach would be to think about these kinds of analytics in operational decisions like shipping additional stock to the point of sale based on predictions that the particular store will soon be out and so on.
• Humana Inc.: Keeping a Watchful Eye on Patients
  In many ways this was a nice example of assisting personnel using analytics and reporting. But what about helping patients self serve? How about influencing the behavior of other providers? If your focus was on improving the operational decisions you could do this. There is some really interesing work being done on EDM in heatlhcare.
• Keystone Automotive: Knowing Which Products Are in Demand
  When I read this I think to myself "do they really need to ask managers to decide?" Could they just not schedule the work? Why only daily? Why not drive the machines and people scheduling too (so as to manage maintenance down-time, staff overtime etc)? A focus on the operational decisions (which part to manufacture next, which staff to call in to handle overtime) and on EDM would deliver potentially much greater benefits. Plus what about predictions and patterns not just demand? Perhaps the drop off in demand yesterday is a good predictor of increase tomorrow? Unless you focus on predicting the future not just on analyzing the past you will miss a key benefit of analytics. This focus on the future is one of the differences between BI and the predictive analytics in EDM.
• PHH Arval: Pinpointing Ways to Save
  Another good performance management scenario. Could EDM, though, offer benefits by improving the scheduling of vehicles or of maintenance? Are there operational day-to-day decisions that could be similarly improved?
• Schneider National: Aligning Purchases with Solid Data.
  A classic strategic decision making problem, BI is the best way to do this for sure.
• United Pipe amp; Supply Co.: Staying Stocked During Disaster
  Again a nice tactical, ad-hoc BI project.
• Virgin Entertainment Group: Getting Schedules in Sync with Customers
  Why make managers do analysis like this? Why not have them focus on their staff and store and customers and automate this? Why not use an EDM approach to bring analytics (of trends, likely future behavior, dominant customer segments using this store) and rules (best practices about how often to change store layout for example) to automate these decisions? Why have a book signer or a manager look at reports when the system could be doing this stuff automatically and letting them focus on their fans and customers. Strikes me that a focus on the actual operational decisions in terms of trying to automate them would have been much more effective for Virgin.

So to summarize

• Some of these were really good examples of applying analytics, not just reporting, to strategic business problems. Classic BI.
• In most of those cases the company could probably get even more value by thinking about applying that insight to operational decisions with EDM.
• Most of the rest of the examples should be using EDM if they really want to get value out of the data - their current focus is leaving a lot on the table.

If you are interested in some of the basics, check out the rules FAQ, predictive analytics FAQ and EDM FAQ sections.

Technorati Tags: analytics, BI, business rule, business rules, CRM, customer experience, decision automation, EDM, predictive analytics, ROI, Enterprise Decision Management

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Posted by James Taylor at 11:26 AM | Comments (0)

September 22, 2006

"Decisions - The Podcast VII" - This time it's predictive!

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Posted by James Taylor at 11:26 AM | Comments (0)

"Decisions - The Podcast VII" - This time it's predictive!

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Posted by James Taylor at 11:26 AM | Comments (0)

"Decisions - The Podcast VII" - This time it's predictive!

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Posted by James Taylor at 11:26 AM | Comments (0)

Blog survey results

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Posted by James Taylor at 10:59 AM | Comments (0)

September 21, 2006

Marketing and Customer Segmentation Becoming Top Priorities Among P&C Insurers

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Posted by James Taylor at 2:43 PM | Comments (0)

September 20, 2006

Posts from the Brainstorm conference

I have been at the Brainstorm conference today and yesterday and blogged continuously (on the other blog). Here are the links if you want to read the posts:

• Live from Brainstorm - BPM/BR Method: Critical Success Factor for the Agile Enterprise
• Live form Brainstorm - Business Rules and Requirements Management at the IRS
• Live from Brainstorm - The Central Role of Leadership in BPM
• Live from Brainstorm - Transforming to a process driven enterprise
• Live from Brainstorm - Business Process Management at the FBI
• Live from Brainstorm - Achieving Operational Excellence with BPM
• Live from Brainstorm - Business Process and Business Rules Panel
• Live from Brainstorm - The vision for Business Rules
• Live from Brainstorm - Making the transition to services engineering
• Live from Brainstorm - Integrating Architecture into Development
• Live from Brainstorm - BPM Suites and SOA
• Live from Brainstorm - Enabling the agile enterprise

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Posted by James Taylor at 1:35 PM | Comments (0)

September 19, 2006

Live from Teradata (almost) - Improving Customer Experience with EDM

I presented at Teradata Partners today on the topic of using enterprise decision management to improve the customer experience. Essentially I proposed that using technology like business rules and analytics to improve the moments of decision when interacting with a customer can improve their experience. Targeting, rewarding loyalty, empowering staff and leveraging information are all part of this. Here's a PDF of the presentation if you want more.

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Posted by James Taylor at 11:46 AM | Comments (0)

September 18, 2006

Live from Teradata Partners - Tom Davenport

I'm at the Teradata Partners conference, just for today, and to have just listened to Tom Davenport (about whom I have blogged before, most recently in this post on decision automation). Tom presented on competing on analytics. Now at Teradata Partners, one is somewhat preaching to the choir when it comes to leveraging analytics. Tom's view is that the planets are aligned for analytics - lots of technology, lots of data, computer-savvy folks, business need for optimized, differentiated processes. While Tom clearly thinks that the folks with Teradata are doing lots right, he differentiated between reporting and analytics as two aspects of Business Intelligence. He premised that reporting is necessary but not sufficient and that analytics delivers more intelligence and more competitive advantage. He proposes that making analytics and fact-based decisioning key to strategy and competition is where the value comes (see this post on strategic alignment)

Tom used some examples of companies to make his point. Marriott is one example that uses analytics aggressively to optimize pricing for revenue management. Harrahs focuses on loyalty to improve profitability and used analytics to transform the business. Tom also used the Red Sox to show that you have to take action based on analytics at the front line by giving the example of the Red Sox manager who failed to apply analytic insight about Pedro Martinez and lost game 7 to the Yankees. Not all companies transform themselves with analytics - Capital One for instance was built by information-based strategies and the use of analytics.

So, if it is possible to compete on analytics, how to do this? Tom has some great ideas and some key requirements.

Technorati Tags: analytics, business rule, business rules, decision automation, knowledge management, predictive analytics, decision yield, teradata

1. Get CEO to commit
   True of most strategies after all. Tom pointed out that intangibles requires a culture that cares and a culture of learning and testing (e.g. Harrah's CEO says that not using a control group will get you fired!). The senior level support is essential so you must demonstrate value and get the leadership on board. This can be particularly hard in companies that like the "manage by gut reaction".
2. Widespread use of modeling and prediction
   This means developing the skills (both skills for developing insight and for using it at front line)
3. Use analytics to support your distinctive capability
   Have a Major and a Minor like Loyalty with Service. Focus on how to compete distinctively - perhaps by using Decision Yield to assess possibilities
4. Manage analytics enterprise-wide
   Hence the "E" in EDM. This is particularly a problem when there are data-fiefdoms. Not only must data be a corporate asset (as Tom noted), so much decisions based on that data. The focus on a core analytics group is something you find in long time analytic users like banks.
5. Have big goals

He identified 5 stages of development in the companies he surveyed:

• Stage 5: Competing on analytics
• Stage 4: Clear intent, almost there - not passionate yet
• Stage 3: Vision but long way to go
• Stage 2: Local, non strategic activity (previously BI best practice)
• Stage 1: Wrestling with the basics

The trend over the last few years is a big growth in above-average analytic capability, though not much growth in competing on analytics yet. Tom has some data suggesting that high performing companies correlate really highly with analytically sophisticated companies - analytic sophistication tends to drive improved performance. Tom made some suggestions for serious analytic competitors that included automating decision processes, knowledge (rules) management and new measures by mining of data. He also talked about event-based/real-time decisioning and how effective it can be. He ended by talking about how this is a long-term plan and I would add that you can get started incrementally - don't wait for all the data, focus on delivering insight from the data as you go!

One last thing. Tom used Katrina as an example of rules and analytics - the analytics said that there was higher than usual occupation in Houston and so prices should be raised but people (rules) said don't raise prices for refugees. Now his example was of manual rules applied to automated analytics but it still shows the need for both rules and analytics.

Great talk. Tom is always worth listening to. If you believe in competing with analytics, you should think about applying analytics to your operational systems and that will lead you to think about Enterprise Decision Management.

If you read this while at the conference, drop by the Fair Isaac booth (#317) and say hi to the folks working there. I'm speaking at 1:30pm too...

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Posted by James Taylor at 7:14 AM | Comments (0)

September 17, 2006

Last chance for the survey

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Posted by James Taylor at 7:38 PM | Comments (0)

Using decisioning to build the bank of the future

I have had some interesting discussions recently around Enterprise Decision Management and banking. These led to the posting"A banking story". It seemed to me, though, that I could articulate more clearly how a The Bank of EDM might act. So, what if your bank...

• always identified you when you put your card in the ATM, called the call center, handed over a check at the teller
• remembered your preferences
• remembered your regular activities and prioritized them
• accurately predicted your likely behavior/needs
• applied constraints and circumstances (ATM wait time, call center wait time, teller v personal banker) to its approach
• used the information you gave them, no matter how you gave it to them
• and so on...

How might that look? Let's consider some different scenarios to see.

Technorati Tags: atm, banking, BRE, BRMS, business rule, business rules, CRM, customer experience, decision automation, IVR, predictive analytics, personalization, website

ATM

When you put your card into the ATM it immediately identified you. Based on your expressed preferences and prior behavior it displayed text in your preferred languages and displayed common actions based on your prior ATM usage. If one particular transaction dominated it might say "do you want to perform your usual action or take some other action". If you have a short list, it might list those with an "or something else" option. If there are transactions you perform often using another channel that can also be performed using this particular ATM it might highlight them in a "did you know you can" section. Besides your explicit historical behavior it might try to predict likely behavior using analytic models. For instance people like you who have a larger than usual checking account balance often want to transfer some of it into savings, so it would offer that action. Obviously it would only let you do things you were allowed - it would not offer you more cash than you have or a larger withdrawal that allowed your account type, would not offer actions on account types you don't have and so on.

It might also make a cross-sell or up-sell offer if it predicts that you would have a reasonable chance of accepting an offer and of not being annoyed by it. It would select an offer based on your behavior and segmentation. Obviously it would only do this when its rules said there was likely no line (using recent gaps between customers at this ATM for example), it would not do so if you had said "no offers" and it would offer to follow-up to close any offer you accepted using your preferred channel (call back from sales person, email, link to web form etc).

Is this how your ATM behaves? Should it be?

Call center/Interactive Voice Response (IVR)

When you call, the system first uses your phone number to see if it can identify you and otherwise asks for your account number. As soon as it has identified you, it asks you to confirm and then proceeds, like the ATM, to offer you options based on your prior behavior and predictions about likely behavior. The choices would be driven by common actions you take and uncommon ones based on the behavior patterns of customers like you. The call may be routed to a person immediately based on the banks need to treat you personally or yours if your preferences and actions match. Again, the treatment you get will depend on data-driven segmentation.

When you do talk to someone they are empowered to act on your behalf(see this banking story or this post on self-service in banking). This means they don't have to refer you to someone else, put you on hold while they ask someone and so on. If they cannot do something, or when there is a long wait, the system will use your preferences and models to say when and how you will be contacted.

How does your call center/IVR experience compare to this one?

Website

You're probably getting the picture now. The website remembers you and your actions/preferences. It displays offers or questions that represent the next best interaction with you, allows you to chat or get a call back based on your preferences, question and status and applies segmentation and models (for wait time, for example). In addition it tracks what you look at and improves both the content display and offers based on this. This information, such as your interest in certain products and not others, is fed back into the models about you and your interests.

Like your website?

Branch

When you go into the branch, the cross-sell offers displayed to the teller use website and other data about you and lead them in guided interaction so that ever teller acts like an experienced facilitator of banking problems. The systems use the current wait time/queue length to drive speed or quality of interaction so that the teller is efficient when there is a line and engaging when there is time. Anything the teller says respects your channel choice e.g. go see the personal banker over there v we'll email you the information.

How's your branch?

Monthly statement

When you get your monthly statement it repeats the offers generated for you intelligently, asks you to call an 800 number to give feedback on your recent branch experience (which it knows you had and knows is unusual for you) and reminds you of upcoming maturity date on a CD by suggesting a roll-over. In addition it helps prevent identity theft by highlighting critical seeming transactions on the first page of the print out (out of pattern transactions).

and so on...

It can be done. It takes a focus on automating and improving operational decisions, the intelligent use of business rules and predictive analytics and a focus on customers. Enterprise Decision Management, in other words.

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Posted by James Taylor at 7:36 PM | Comments (0)

September 15, 2006

If 5% of business is unique, is it all decisioning?

Shai Agassi recently presented on SAP's roadmap and one of his key points was how much of business was common. As CRMchump said:

“"Dive strategic differentiation" got a bit more play from Agassi, who interestingly claimed that "more than 95 percent of business is common across all companies, in all industries." This five percent difference is what provides the strategic differentiation that should be exploited, and ultimately run on an SOA system.

I completely agree with Shai on this one (or should I say he completely agrees with me). Take a typical business process and almost every step has a best practice and/or template. In fact the rush to commoditized business processes is something about which I have written before. However the way take decisions within that process is unique to you. It also remains the only way I know to truly differentiate outsourced processes. So what makes decisions unique to you?

• Customer segmentation is based on your data and your profiles
• Your policies and procedures are unique, even if they build on regulations and external rules
• The data that drives personalization is yours and the insight from that data cannot be duplicated by someone else
• Your business users have their own experience and that experience uniquely informs the rules they write, the way they treat customers etc.

So is the whole of the 5% decisions? Who knows. But decisions are certainly not common across companies and so automating and improving them using enterprise decision management to bring your rules and your analytic insights to bear will give you strategic differentiation.

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Posted by James Taylor at 11:02 AM | Comments (0)

September 14, 2006

Using knowledge management and business rules to avoid testifying to congress

Great post by Rolando Hernandez about Knowledge Management is Key to Preventing Brain Drain. BP found out the hard way.He uses some nice contrasting stories about BP's problems with its oil pipeline, caused in part by a failure to manage expert knowledge when someone left, and another oil company's use of knowledge management to avoid a similar problem in another area. This is a common problem, especially as the baby boom retire, and a survey by Barb von Halle's KPI found it to be one of the top reasons for adopting a business rules management approach. Not only would a business rules approach be a great way to capture the knowledge, the use of a business rules management system to manage and deliver that knowledge would let you automate, or at least support, those expert decisions so that customers, front-line staff, other information systems could leverage it.

Don't forget the blog survey - http://www.zoomerang.com/survey.zgi?p=WEB225MZ6RJRFG- please take it so I know what you think about the blog. Thanks.

Technorati Tags: BRMS, business rule, business rules, knowledge management

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Posted by James Taylor at 11:50 AM | Comments (0)

Moving to "smart" composite Enterprise Marketing Management applications

Kimberley Collins over at Gartner recently wrote a piece on Enterprise Marketing Management: Moving from Suites to Composite Applications. I found this interesting as the interaction of marketing decision management and marketing management software is a regular topic of conversation among marketing-focused enterprise decision management thinkers.

Kimberley's piece was interesting as it made it clear that the future of Enterprise Marketing Management was in composite applications and that business rules and predictive analytics are both going to show up in this future state. Not only are business rules and predictive analytics components common to many processes within marketing, they are also commonly used to improve CRM, customer experience and service also. She also identified the value of having business people take control of the rules, and processes, involved something for which business rules management systems are ideal.

Regular readers will know that I believe strongly that combining business rules (potentially expert-driven, potentially data mined) with predictive analytics into decision services is the way to go. In Kimberley's stack she links predictive analytics to reporting and business rules to workflow. Linking workflow and rules like this could lead to over-synchronization. Kimberley's layer of analytical tools seemed mostly focused on helping marketing knowledge workers do a better job. While this is necessary step, I believe driving analytic insight into automation will further leverage the knowledge, experience and data being captured. In the model she proposes I might have put "decisions" or at least "operational decisions" into the knowledge management layer and considered both analytics and rules as ways to automate these decisions. Automation allows the website, the IVR system, the call center, the branch and more to deliver the next best marketing decision without manual intervention. I also believe that making these decisions available as atomic services will enable them to be reused outside of marketing processes for maximum value.

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